Recently, methods of performing object identification and the like using machine learning are being studied. As one of the machine learning, a deep learning, which uses a neural network with several hidden layers between an input layer and an output layer, shows high recognizing performance.
And, the neural network using the deep learning generally learns through backpropagation using losses.
In order to perform learning of such a deep learning network, training data in which tags are added to individual data points by labelers are needed. Preparing this training data (i.e. classifying the data correctly) can be very labour-intensive, expensive and inconvenient, especially if a large amount of training data is to be used and if the quality of the data pre-preparation is not consistently high. Conventional interactive labeling can be computationally expensive and fail to deliver good results.
Therefore, in recent years, auto-labeling which adds tags, i.e., labels, to a training image using a deep learning-based auto labeling device is performed, and inspectors examine auto-labeled training images to correct the tags or the labels.
In the conventional auto-labeling, images are enlarged for accurate detection of small-sized objects, but enlarging sizes of the images causes increase of computational load.
Conversely, if the images are used as is to reduce the computational load, the small-sized objects cannot be detected properly, and thus accuracy decreases.
Accordingly, the inventors of the present disclosure propose an auto-labeling method capable of maintaining the accuracy and reducing the computational load.